Modern manufacturing factories generate a steady stream of complex, heterogeneous factory data collected from various types of sensors distributed throughout the manufacturing factories. Such data is key for improving operations and product quality and for addressing manufacturing problems, such as inefficiencies and underperformance attributed to machine downtimes, supply chain interruptions, and poor quality among others. However, traditional approaches for using the data are limited and cumbersome. Traditional approaches are also reactive in the sense that manufacturers typically wait until a problem has been identified before investigating and collecting data to identify the cause of the problem. As a result, there can be significant lag times between when a manufacturer is aware of a problem and when solutions are finally implemented. As another example, there can be difficulties in tracing problems that are detected in a final completed product to specific root causes among the many machines and processes. These difficulties are caused or exacerbated by challenges in extracting information and knowledge that may be hidden amongst a diverse and large amount of factory data. Manufacturing problems, especially if identified too late, may require costly corrective measures, such as additional product inspections, warranty claims and recalls, reworking products, and so on. Therefore, there is a need for a faster, more real-time, or predictive approach to effectively using the factory data and providing actionable solutions to manufacturers for improving their operations and product quality.
This application is intended to address such issues and to provide related advantages.